JMIR Publications

ABSTRACT

Background:

The increasing demand on healthcare systems requires innovative and transformative solutions to deliver efficient and high-quality care. One promising approach is Digital Twin (DT) technology, which leverages real time data to create dynamic virtual representations of a physical space, entity or individuals to anticipate future scenarios and support care decisions. While DTs have been explored in various sectors, their application in Hospital at Home (HaH) settings remains unexplored. HaH allows patients to receive acute-level care at home and given the acuity and complexity of care delivered in this setting, this scoping review bridges a critical knowledge gap by identifying the key DT-enabling architecture to inform a hospital-at-home DT and relevant applications and data types.

Objective:

This review aimed to examine the existing evidence on DT-enabling tools for managing patients with frailty in home settings and identify the underpinning architectural components required to support a hospital-at-home DT system.

Methods:

A systematic search was conducted across six electronic databases, and relevant websites for grey literature, for primary studies published in English between 2019 and September 2024.

Results:

Sixty-two studies were analysed for their DT-enabling tools and systematically mapped across the proposed five-layered DT architecture: sensing, communication, storage, analytics, and visualisation. Taxonomies of DT layers, their interconnections, and the classifications of the types of data collected (e.g., about the patient, the home environment, the use of medical equipment) are presented. The available evidence presents a range of sensing technologies, both for gathering patient data and the environment, however greater understanding of data management (communication and storage) methods is needed, particularly in the context of local healthcare systems. Moreover, the findings highlight emerging potential of predictive and prescriptive analytics to be used to enable clinicians to predict risk, support clinical decision-making, or activate alert-triggered health interventions. Albeit, evidence in this field suggests that analytics methods are largely descriptive and there is a shortage of advanced methods such as prescriptive analytics to enable recommendations of an optimal course of action, and a lack of diagnostic analytics which can highlight why a situation has occurred. Reported DT-enabling tools have demonstrated a wealth of opportunities including patient-centered benefits such as enhanced patient motivation, patient/ carer reassurance and personalised care. However, concerns were also reported on device accuracy, usability, acceptability and the broader implications on carers and organisational systems.

Conclusions:

This review offers the first comprehensive synthesis of DT-enabling tools relevant to inform a potential HaH-DT. By outlining the key conceptual architecture, these insights lay the foundations that will enable stakeholders to effectively leverage technology-enabled care in complex home-based settings to deliver safer, more efficient, personalised and timely care. Clinical Trial: Trial Registration: BMJ Open; https://bmjopen.bmj.com/content/15/6/e093418 [1]